Statistical Analysis of Empirical Data - Scott Pardo

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Résumé :
Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method. Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.

Biographie:
Scott A. Pardo, Ph.D., is a professional statistician, having worked in a wide variety of industrial contexts, including the U.S. Army Information Systems Command, satellite systems engineering, pharmaceutical development, and medical devices. He is the author of Empirical Modeling and Data Analysis for Engineers and Applied Scientists (Springer 2016). He is a Six Sigma Master Black Belt, an Accredited Professional Statistician (PStat(TM)), and holds a Ph.D. in Industrial and Systems Engineering from the University of Southern California....

Sommaire:
Chapter 1: Fundamentals.- Chapter 2: Sample Statistics are NOT Parameters.- Chapter 3: Confidence.- Chapter 4: Multiplicity and Multiple Comparisons.- Chapter 5: Power and the Myth of Sample Size Determination.- Chapter 6: Regression and Model Fitting with Collinearity.- Chapter 7: Overparameterization.- Chapter 8: Ignoring Error Control Factors and Experimental Design.- Chapter 9: Generalized Linear Models.- Chapter 10: Mixed Models and Variance Components.- Chapter 11: Models, Models Everywhere...Model Selection.- Chapter 12: Bayesian Analyses.- Chapter 13: The Acceptance Sampling Game.- Chapter 14: Nonparametric Statistics - A Strange Name.- Chapter 15: Autocorrelated Data and Dynamic Systems.- Chapter 16: Multivariate Analysis and Classification.- Chapter 17: Time-to-Event: Survival and Life Testing.- Index.